Parallel Optimization Framework for Cloud-Based Small Cell Networks

Cloud-based small cell networks (C-SCNs) have recently been proposed as new wireless cellular architecture. In cloud-based networks, optimization of radio resources at the base station (BS) is moved to a cloud data center for centralized optimization. In the center, multiple processors referred to as the cloud computational unit (CCU) are used for the optimization. As the cell size and networks become, respectively, smaller and denser, the number of BSs to be optimized grows exponentially, resulting in high computational complexity and latency at CCUs. In this paper, we propose belief propagation-based power allocation schemes for C-SCNs that can be used for any network optimization objectives, such as energy consumption minimization at the data center and BSs, and spectral efficiency. The computation for the schemes is distributed across multiple processors and done in parallel, leading to very low latency and computational complexity with increasing number of BSs. We prove mathematically that the messages of the proposed algorithms converge to a fixed point and show via simulation that their performances in terms of spectral and energy efficiencies are close to an exhaustive search solution in finding the best configuration.